Pandas: Create New DataFrame Based on Column Value

Table of Contents

1. Core Concepts
2. Typical Usage Method
3. Common Practices
4. Best Practices
5. Code Examples
6. Conclusion
7. FAQ
8. References

Core Concepts

DataFrame

A DataFrame in Pandas is a two - dimensional labeled data structure with columns of potentially different types. It can be thought of as a table, similar to a spreadsheet or a SQL table.

Column Value Filtering

Column value filtering is the process of selecting rows from a DataFrame based on the values in one or more columns. When we create a new DataFrame based on column values, we are essentially subsetting the original DataFrame by applying a condition to one or more columns.

Typical Usage Method

The most straightforward way to create a new DataFrame based on column values is by using boolean indexing. Boolean indexing allows us to select rows from a DataFrame where a certain condition is True.

The general syntax is as follows:

new_df = original_df[original_df['column_name'] == value]

Here, original_df is the original DataFrame, column_name is the name of the column we want to filter on, and value is the specific value we are looking for.

Common Practices

Filtering with Multiple Conditions

Often, we need to filter a DataFrame based on multiple conditions. We can use logical operators such as & (and) and | (or) to combine multiple conditions.

new_df = original_df[(original_df['column1'] == value1) & (original_df['column2'] > value2)]

Filtering with Categorical Data

When working with categorical columns, we can filter based on specific categories.

new_df = original_df[original_df['category_column'].isin(['category1', 'category2'])]

Best Practices

Use .loc for Label - Based Indexing

While boolean indexing works well, using .loc is more explicit and can prevent some potential issues, especially when dealing with mixed integer and label - based indexing.

new_df = original_df.loc[original_df['column_name'] == value]

Check for Null Values

Before filtering, it’s a good practice to check for null values in the column we are filtering on. We can use the .notna() method to exclude rows with null values.

new_df = original_df[original_df['column_name'].notna() & (original_df['column_name'] == value)]

Code Examples

import pandas as pd

# Create a sample DataFrame
data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'David'],
    'Age': [25, 30, 35, 40],
    'City': ['New York', 'Los Angeles', 'Chicago', 'New York']
}
original_df = pd.DataFrame(data)

# Create a new DataFrame based on a single condition
new_df_single = original_df[original_df['City'] == 'New York']
print("New DataFrame based on single condition:")
print(new_df_single)

# Create a new DataFrame based on multiple conditions
new_df_multiple = original_df[(original_df['Age'] > 30) & (original_df['City'] == 'New York')]
print("\nNew DataFrame based on multiple conditions:")
print(new_df_multiple)

# Use .loc for label - based indexing
new_df_loc = original_df.loc[original_df['Age'] < 35]
print("\nNew DataFrame using .loc:")
print(new_df_loc)

# Filtering with categorical data
new_df_categorical = original_df[original_df['City'].isin(['New York', 'Chicago'])]
print("\nNew DataFrame based on categorical data:")
print(new_df_categorical)

# Check for null values
original_df_with_null = original_df.copy()
original_df_with_null.loc[2, 'Age'] = None
new_df_not_null = original_df_with_null[original_df_with_null['Age'].notna() & (original_df_with_null['Age'] < 35)]
print("\nNew DataFrame after checking for null values:")
print(new_df_not_null)

Conclusion

Creating a new DataFrame based on column values is a fundamental operation in Pandas. By understanding the core concepts, typical usage methods, common practices, and best practices, intermediate - to - advanced Python developers can effectively filter and subset data for various data analysis tasks. Boolean indexing, combined with logical operators and .loc for label - based indexing, provides a flexible and powerful way to create new DataFrames based on column values.

FAQ

Q1: Can I filter a DataFrame based on a range of values?

Yes, you can use comparison operators such as > and < to filter based on a range of values. For example, original_df[(original_df['column_name'] > min_value) & (original_df['column_name'] < max_value)].

Q2: What if I want to filter a DataFrame based on a condition in one column and select specific columns from the result?

You can use .loc to specify both the row and column selection. For example, original_df.loc[original_df['column1'] == value, ['column2', 'column3']].

Q3: How can I filter a DataFrame based on a regular expression in a string column?

You can use the .str.contains() method. For example, original_df[original_df['string_column'].str.contains('pattern', na=False)].

References

• Pandas official documentation: https://pandas.pydata.org/docs/
• Python Data Science Handbook by Jake VanderPlas

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